code: def test_scoring(
      scheduler,
      xy_classification,
      scoring=dask_ml.metrics.accuracy_score,
  ):
      X, y = xy_classification
      with scheduler() as (s, [a, b]):
          clf = Incremental(SGDClassifier(tol=1e-3), scoring=scoring)
          with pytest.raises(
              ValueError, match="metric function rather than a scorer"
          ):
              clf.fit(X, y, classes=np.unique(y))
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  import sklearn.model_selection
  from dask.array.utils import assert_eq
  from scipy.sparse import csr_matrix
  from sklearn.base import clone
  from sklearn.linear_model import SGDClassifier, SGDRegressor
  from sklearn.pipeline import make_pipeline
  import dask_ml.feature_extraction.text
  import dask_ml.metrics
  from dask_ml.metrics.scorer import check_scoring
  from dask_ml.wrappers import Incremental
label_desc: Use image node_id 5 for calling a global function with example usage: test_scoring(scheduler, xy_classification, scoring) without return types